Overview

Dataset statistics

Number of variables10
Number of observations132860
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.2 MiB
Average record size in memory191.0 B

Variable types

DateTime1
Numeric8
Text1

Alerts

tavg is highly overall correlated with tmax and 1 other fieldsHigh correlation
tmax is highly overall correlated with tavg and 1 other fieldsHigh correlation
tmin is highly overall correlated with tavg and 1 other fieldsHigh correlation
tmin has 2299 (1.7%) zerosZeros
prcp has 87434 (65.8%) zerosZeros
snow has 128348 (96.6%) zerosZeros

Reproduction

Analysis started2024-06-17 11:54:04.063209
Analysis finished2024-06-17 11:54:08.827965
Duration4.76 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

time
Date

Distinct365
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Minimum2023-01-01 00:00:00
Maximum2023-12-31 00:00:00
2024-06-17T14:54:08.867431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:08.940711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

tavg
Real number (ℝ)

HIGH CORRELATION 

Distinct798
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.042641
Minimum-39.1
Maximum42.2
Zeros208
Zeros (%)0.2%
Negative13875
Negative (%)10.4%
Memory size1.0 MiB
2024-06-17T14:54:09.011063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-39.1
5-th percentile-4.1
Q16.3
median15.4
Q322.5
95-th percentile28.9
Maximum42.2
Range81.3
Interquartile range (IQR)16.2

Descriptive statistics

Standard deviation10.672859
Coefficient of variation (CV)0.76003217
Kurtosis-0.073791662
Mean14.042641
Median Absolute Deviation (MAD)7.9
Skewness-0.51108309
Sum1865705.3
Variance113.90992
MonotonicityNot monotonic
2024-06-17T14:54:09.175737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.9 565
 
0.4%
22.3 557
 
0.4%
23.1 556
 
0.4%
21.8 548
 
0.4%
22.6 544
 
0.4%
22.8 542
 
0.4%
20.6 542
 
0.4%
24.2 538
 
0.4%
20.3 537
 
0.4%
21.4 536
 
0.4%
Other values (788) 127395
95.9%
ValueCountFrequency (%)
-39.1 1
< 0.1%
-36.7 1
< 0.1%
-36.4 1
< 0.1%
-36 1
< 0.1%
-35.6 1
< 0.1%
-35.2 2
< 0.1%
-35 1
< 0.1%
-34.8 1
< 0.1%
-34.6 1
< 0.1%
-34.3 1
< 0.1%
ValueCountFrequency (%)
42.2 4
< 0.1%
41.3 2
 
< 0.1%
41.1 2
 
< 0.1%
40.9 2
 
< 0.1%
40.7 2
 
< 0.1%
40.6 2
 
< 0.1%
40.4 4
< 0.1%
40.3 9
< 0.1%
40.2 7
< 0.1%
39.9 4
< 0.1%

tmin
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct674
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6998325
Minimum-76
Maximum37.2
Zeros2299
Zeros (%)1.7%
Negative27827
Negative (%)20.9%
Memory size1.0 MiB
2024-06-17T14:54:09.257806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-76
5-th percentile-9.105
Q11.1
median9.4
Q317.2
95-th percentile24.4
Maximum37.2
Range113.2
Interquartile range (IQR)16.1

Descriptive statistics

Standard deviation10.680441
Coefficient of variation (CV)1.2276606
Kurtosis0.041017651
Mean8.6998325
Median Absolute Deviation (MAD)7.8
Skewness-0.45497794
Sum1155859.8
Variance114.07183
MonotonicityNot monotonic
2024-06-17T14:54:09.338507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 2700
 
2.0%
11.7 2392
 
1.8%
15 2376
 
1.8%
0 2299
 
1.7%
13.9 2286
 
1.7%
12.2 2277
 
1.7%
14.4 2204
 
1.7%
13.3 2191
 
1.6%
10.6 2191
 
1.6%
8.3 2162
 
1.6%
Other values (664) 109782
82.6%
ValueCountFrequency (%)
-76 1
 
< 0.1%
-41.1 1
 
< 0.1%
-40.6 1
 
< 0.1%
-40 2
< 0.1%
-39 4
< 0.1%
-38.9 1
 
< 0.1%
-38.8 1
 
< 0.1%
-38.3 1
 
< 0.1%
-38.2 1
 
< 0.1%
-38 2
< 0.1%
ValueCountFrequency (%)
37.2 1
 
< 0.1%
36.1 2
 
< 0.1%
35.6 2
 
< 0.1%
35 6
 
< 0.1%
34.4 8
 
< 0.1%
33.9 20
< 0.1%
33.3 13
< 0.1%
33 1
 
< 0.1%
32.9 1
 
< 0.1%
32.8 18
< 0.1%

tmax
Real number (ℝ)

HIGH CORRELATION 

Distinct675
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.617149
Minimum-35
Maximum50
Zeros827
Zeros (%)0.6%
Negative6290
Negative (%)4.7%
Memory size1.0 MiB
2024-06-17T14:54:09.441111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-35
5-th percentile0
Q111.7
median21.2
Q328.9
95-th percentile35
Maximum50
Range85
Interquartile range (IQR)17.2

Descriptive statistics

Standard deviation11.392223
Coefficient of variation (CV)0.58072775
Kurtosis-0.191582
Mean19.617149
Median Absolute Deviation (MAD)8.2
Skewness-0.52030681
Sum2606334.5
Variance129.78275
MonotonicityNot monotonic
2024-06-17T14:54:09.536537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 2943
 
2.2%
28.3 2760
 
2.1%
29.4 2656
 
2.0%
27.8 2639
 
2.0%
28.9 2638
 
2.0%
27.2 2616
 
2.0%
25 2522
 
1.9%
30.6 2439
 
1.8%
26.7 2429
 
1.8%
26.1 2390
 
1.8%
Other values (665) 106828
80.4%
ValueCountFrequency (%)
-35 1
 
< 0.1%
-33.3 2
< 0.1%
-32.8 1
 
< 0.1%
-32 1
 
< 0.1%
-31.7 1
 
< 0.1%
-31.1 2
< 0.1%
-30 4
< 0.1%
-29.4 1
 
< 0.1%
-29 1
 
< 0.1%
-28.9 2
< 0.1%
ValueCountFrequency (%)
50 1
 
< 0.1%
48.9 1
 
< 0.1%
48.3 6
 
< 0.1%
48 1
 
< 0.1%
47.8 12
< 0.1%
47.2 5
 
< 0.1%
47 1
 
< 0.1%
46.7 21
< 0.1%
46.1 23
< 0.1%
46 2
 
< 0.1%

prcp
Real number (ℝ)

ZEROS 

Distinct812
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5206044
Minimum0
Maximum571.5
Zeros87434
Zeros (%)65.8%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-06-17T14:54:09.616458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile14.7
Maximum571.5
Range571.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.9157547
Coefficient of variation (CV)3.1404193
Kurtosis298.08058
Mean2.5206044
Median Absolute Deviation (MAD)0
Skewness9.616785
Sum334887.5
Variance62.659172
MonotonicityNot monotonic
2024-06-17T14:54:09.698596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 87434
65.8%
0.3 3962
 
3.0%
0.5 2477
 
1.9%
0.8 1864
 
1.4%
1 1456
 
1.1%
1.3 1242
 
0.9%
1.5 1048
 
0.8%
1.8 1015
 
0.8%
0.2 898
 
0.7%
2 802
 
0.6%
Other values (802) 30662
 
23.1%
ValueCountFrequency (%)
0 87434
65.8%
0.1 792
 
0.6%
0.15 2
 
< 0.1%
0.2 898
 
0.7%
0.25 1
 
< 0.1%
0.3 3962
 
3.0%
0.35 1
 
< 0.1%
0.4 566
 
0.4%
0.45 2
 
< 0.1%
0.5 2477
 
1.9%
ValueCountFrequency (%)
571.5 1
 
< 0.1%
316.7 1
 
< 0.1%
282.7 1
 
< 0.1%
242.1 1
 
< 0.1%
225 1
 
< 0.1%
204.5 1
 
< 0.1%
191.3 1
 
< 0.1%
177.8 1
 
< 0.1%
176.5 1
 
< 0.1%
169.9 3
< 0.1%

snow
Real number (ℝ)

ZEROS 

Distinct89
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.810131
Minimum0
Maximum1780
Zeros128348
Zeros (%)96.6%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-06-17T14:54:09.773453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1780
Range1780
Interquartile range (IQR)0

Descriptive statistics

Standard deviation50.935283
Coefficient of variation (CV)7.4793397
Kurtosis146.63919
Mean6.810131
Median Absolute Deviation (MAD)0
Skewness10.646904
Sum904794
Variance2594.4031
MonotonicityNot monotonic
2024-06-17T14:54:09.846847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 128348
96.6%
30 800
 
0.6%
50 483
 
0.4%
80 337
 
0.3%
100 302
 
0.2%
130 259
 
0.2%
180 181
 
0.1%
150 179
 
0.1%
300 144
 
0.1%
250 136
 
0.1%
Other values (79) 1691
 
1.3%
ValueCountFrequency (%)
0 128348
96.6%
10 7
 
< 0.1%
20 7
 
< 0.1%
25 49
 
< 0.1%
30 800
 
0.6%
40 4
 
< 0.1%
50 483
 
0.4%
51 28
 
< 0.1%
60 2
 
< 0.1%
70 2
 
< 0.1%
ValueCountFrequency (%)
1780 1
 
< 0.1%
1520 5
 
< 0.1%
1270 1
 
< 0.1%
940 2
 
< 0.1%
910 2
 
< 0.1%
890 8
< 0.1%
860 6
< 0.1%
840 14
< 0.1%
810 14
< 0.1%
790 10
< 0.1%

wdir
Real number (ℝ)

Distinct507
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.68604
Minimum0
Maximum360
Zeros598
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-06-17T14:54:09.930325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q179
median197
Q3297
95-th percentile349
Maximum360
Range360
Interquartile range (IQR)218

Descriptive statistics

Standard deviation114.6291
Coefficient of variation (CV)0.60751234
Kurtosis-1.3637998
Mean188.68604
Median Absolute Deviation (MAD)108
Skewness-0.13140983
Sum25068828
Variance13139.83
MonotonicityNot monotonic
2024-06-17T14:54:10.004721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
352 678
 
0.5%
342 627
 
0.5%
340 626
 
0.5%
346 623
 
0.5%
336 615
 
0.5%
355 614
 
0.5%
350 614
 
0.5%
2 613
 
0.5%
344 611
 
0.5%
345 611
 
0.5%
Other values (497) 126628
95.3%
ValueCountFrequency (%)
0 598
0.5%
1 591
0.4%
2 613
0.5%
3 550
0.4%
4 571
0.4%
5 556
0.4%
6 552
0.4%
7 552
0.4%
8 537
0.4%
9 562
0.4%
ValueCountFrequency (%)
360 44
 
< 0.1%
359 600
0.5%
358 576
0.4%
357 603
0.5%
356 581
0.4%
355 614
0.5%
354 557
0.4%
353 602
0.5%
352 678
0.5%
351 584
0.4%

wspd
Real number (ℝ)

Distinct517
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.424556
Minimum0
Maximum78.3
Zeros34
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-06-17T14:54:10.078616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.3
Q17.9
median11.3
Q315.7
95-th percentile24.1
Maximum78.3
Range78.3
Interquartile range (IQR)7.8

Descriptive statistics

Standard deviation6.2112607
Coefficient of variation (CV)0.49991812
Kurtosis1.9571992
Mean12.424556
Median Absolute Deviation (MAD)3.7
Skewness1.0698663
Sum1650726.5
Variance38.579759
MonotonicityNot monotonic
2024-06-17T14:54:10.163306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4 3301
 
2.5%
10.8 2787
 
2.1%
7.9 2632
 
2.0%
12.2 2532
 
1.9%
11.2 2362
 
1.8%
13.7 2189
 
1.6%
7.6 2142
 
1.6%
10.1 2119
 
1.6%
8.6 2113
 
1.6%
9.7 2096
 
1.6%
Other values (507) 108587
81.7%
ValueCountFrequency (%)
0 34
< 0.1%
0.2 11
 
< 0.1%
0.3 7
 
< 0.1%
0.4 35
< 0.1%
0.5 9
 
< 0.1%
0.6 4
 
< 0.1%
0.7 67
0.1%
0.8 13
 
< 0.1%
0.9 13
 
< 0.1%
1 13
 
< 0.1%
ValueCountFrequency (%)
78.3 1
< 0.1%
66.6 1
< 0.1%
62.3 1
< 0.1%
60.8 1
< 0.1%
59.4 1
< 0.1%
59.3 1
< 0.1%
56.9 1
< 0.1%
56.2 1
< 0.1%
55.8 1
< 0.1%
55.4 2
< 0.1%

pres
Real number (ℝ)

Distinct773
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1015.6504
Minimum964.3
Maximum1051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-06-17T14:54:10.241652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum964.3
5-th percentile1005
Q11011.6
median1015.4
Q31019.7
95-th percentile1027.4
Maximum1051
Range86.7
Interquartile range (IQR)8.1

Descriptive statistics

Standard deviation6.9445985
Coefficient of variation (CV)0.0068375878
Kurtosis1.6793718
Mean1015.6504
Median Absolute Deviation (MAD)4.1
Skewness-0.18209675
Sum1.3493931 × 108
Variance48.227449
MonotonicityNot monotonic
2024-06-17T14:54:10.400339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1014.4 992
 
0.7%
1014.9 978
 
0.7%
1013.9 972
 
0.7%
1015.5 958
 
0.7%
1014.2 957
 
0.7%
1013.8 944
 
0.7%
1013.4 943
 
0.7%
1015.3 941
 
0.7%
1014.5 935
 
0.7%
1015.8 929
 
0.7%
Other values (763) 123311
92.8%
ValueCountFrequency (%)
964.3 1
< 0.1%
964.7 1
< 0.1%
967.7 1
< 0.1%
968.6 1
< 0.1%
969.8 1
< 0.1%
970.1 1
< 0.1%
971 1
< 0.1%
971.2 1
< 0.1%
971.5 1
< 0.1%
971.7 1
< 0.1%
ValueCountFrequency (%)
1051 1
< 0.1%
1049.2 1
< 0.1%
1047.2 1
< 0.1%
1047.1 1
< 0.1%
1046.9 1
< 0.1%
1046.1 2
< 0.1%
1045.8 1
< 0.1%
1045.7 1
< 0.1%
1045.3 1
< 0.1%
1044.6 1
< 0.1%
Distinct364
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
2024-06-17T14:54:10.584548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters398580
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowABE
2nd rowABE
3rd rowABE
4th rowABE
5th rowABE
ValueCountFrequency (%)
abe 365
 
0.3%
atl 365
 
0.3%
abr 365
 
0.3%
aby 365
 
0.3%
ack 365
 
0.3%
act 365
 
0.3%
acv 365
 
0.3%
acy 365
 
0.3%
adk 365
 
0.3%
adq 365
 
0.3%
Other values (354) 129210
97.3%
2024-06-17T14:54:10.846759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 32485
 
8.2%
A 30660
 
7.7%
C 24455
 
6.1%
L 24090
 
6.0%
T 22630
 
5.7%
B 21900
 
5.5%
M 21535
 
5.4%
R 19345
 
4.9%
P 18250
 
4.6%
I 17885
 
4.5%
Other values (16) 165345
41.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 398580
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 32485
 
8.2%
A 30660
 
7.7%
C 24455
 
6.1%
L 24090
 
6.0%
T 22630
 
5.7%
B 21900
 
5.5%
M 21535
 
5.4%
R 19345
 
4.9%
P 18250
 
4.6%
I 17885
 
4.5%
Other values (16) 165345
41.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 398580
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 32485
 
8.2%
A 30660
 
7.7%
C 24455
 
6.1%
L 24090
 
6.0%
T 22630
 
5.7%
B 21900
 
5.5%
M 21535
 
5.4%
R 19345
 
4.9%
P 18250
 
4.6%
I 17885
 
4.5%
Other values (16) 165345
41.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 398580
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 32485
 
8.2%
A 30660
 
7.7%
C 24455
 
6.1%
L 24090
 
6.0%
T 22630
 
5.7%
B 21900
 
5.5%
M 21535
 
5.4%
R 19345
 
4.9%
P 18250
 
4.6%
I 17885
 
4.5%
Other values (16) 165345
41.5%

Interactions

2024-06-17T14:54:08.161926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:04.792577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:05.291790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:05.737923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:06.172028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:06.637232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:07.181946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:07.687854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:08.221591image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:04.853042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:05.351224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:05.798908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:06.232073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:06.700861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:07.241204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:07.750857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:08.282975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:04.909603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:05.404694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:05.852957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:06.292285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:06.757462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:07.332672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:07.809685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:08.332585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:04.962409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:05.454910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:05.899713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:06.343888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:06.809753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:07.385675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:07.864837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:08.385735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:05.019487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:05.510791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:05.952454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:06.402115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:06.866177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:07.443169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:07.923549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:08.444894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:05.078130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:05.568969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:06.008691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:06.458174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:06.923961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:07.502151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:07.983331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:08.502248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:05.171899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:05.625529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:06.065203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:06.522876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:07.068609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:07.563668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:08.047440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:08.559638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:05.237175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:05.686342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:06.122510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:06.583339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:07.128483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:07.631068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-17T14:54:08.109304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-06-17T14:54:10.904014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
prcppressnowtavgtmaxtminwdirwspd
prcp1.000-0.2600.018-0.021-0.1130.070-0.0860.139
pres-0.2601.0000.025-0.269-0.237-0.276-0.056-0.224
snow0.0180.0251.000-0.276-0.275-0.2680.0210.039
tavg-0.021-0.269-0.2761.0000.9700.969-0.104-0.048
tmax-0.113-0.237-0.2750.9701.0000.899-0.086-0.099
tmin0.070-0.276-0.2680.9690.8991.000-0.123-0.020
wdir-0.086-0.0560.021-0.104-0.086-0.1231.0000.004
wspd0.139-0.2240.039-0.048-0.099-0.0200.0041.000

Missing values

2024-06-17T14:54:08.630555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-17T14:54:08.739284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

timetavgtmintmaxprcpsnowwdirwspdpresairport_id
02023-01-018.12.211.70.00.0278.09.71013.8ABE
12023-01-025.40.011.70.00.0353.03.61019.6ABE
22023-01-038.47.29.415.20.050.05.01013.9ABE
32023-01-0411.16.717.20.00.0302.04.71009.8ABE
42023-01-0512.76.714.47.90.0292.07.21013.0ABE
52023-01-065.82.87.25.80.0308.09.01016.6ABE
62023-01-073.81.76.70.00.0285.011.21022.2ABE
72023-01-082.2-2.73.90.00.0346.06.81024.5ABE
82023-01-092.3-1.05.00.00.0271.011.91015.2ABE
92023-01-101.9-1.64.40.00.0250.09.41017.4ABE
timetavgtmintmaxprcpsnowwdirwspdpresairport_id
1328502023-12-2214.412.016.18.60.053.013.91012.8YUM
1328512023-12-2313.89.018.31.20.049.04.21014.9YUM
1328522023-12-2414.010.018.90.00.016.07.51019.4YUM
1328532023-12-2513.18.418.30.00.00.09.41020.5YUM
1328542023-12-2612.88.019.40.00.011.08.41020.3YUM
1328552023-12-2712.26.717.60.00.030.09.61021.0YUM
1328562023-12-2812.86.721.00.00.012.011.11020.6YUM
1328572023-12-2913.67.020.20.00.022.013.71020.0YUM
1328582023-12-3013.67.021.00.00.0337.011.21016.5YUM
1328592023-12-3112.88.019.00.00.033.09.51019.0YUM